In the domain of machine learning and software engineering, understanding and representing source code using neural networks has become an important approach. Different approaches make use of different characteristics present in programming languages. One such characteristic is the rich syntactic information found in the source code's abstract syntax tree (AST). Code2seq [1]} passes paths in the AST through an encoder-decoder network to represent code. The graph structure of AST has been exploited in other research for source code representation on downstream tasks such as variable misuse detection, code generation, natural language code search and program repair [2]}, [3]}, [4]}, [5]}.
Source code text itself is used in models such as CodeBERT [6]}, CuBERT [7]} and DeepFix [8]} for use in tasks such as natural language code search, finding function-docstring mismatch and program repair.
